Fault arc detection method and system based on frequency band analysis and parameter condition judgment

Abstract

The invention relates to the technical field of electrical safety detection, in particular to a fault arc detection method and system based on frequency band analysis and parameter condition judgment, and the method comprises the steps: collecting a current signal according to a set collection period, and carrying out the current data preprocessing of the collected current data of each period; after preprocessing is completed, feature extraction is conducted on the current data, frequency band analysis and condition judgment are conducted according to the extracted features, and whether the fault arc existence condition is met or not is judged; when the fault arc existence condition is met, the fault period is accumulated and reduced through a fault period accumulation mechanism and a fault period reduction machine; and dynamically adjusting a fault judgment period threshold according to the RMS value of the current, and judging whether a fault arc exists or not according to the dynamically adjusted fault judgment period threshold. According to the invention, high-efficiency and accurate fault arc detection is realized by combining current amplitude change analysis and condition judgment of each frequency band.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical safety detection, and in particular to a fault arc detection method and system based on frequency band analysis and parameter condition judgment. Background Art

[0002] Arc fault detection technology is a safety technology for preventing electrical fires. Arc faults are typically caused by short circuits, aging lines, poor line contact, or load failures. Arc faults are characterized by their long duration and concentrated energy, making them highly susceptible to fires. However, conventional circuit protection devices are unable to detect these types of faults. Therefore, specialized detection methods are required for these circuit faults.

[0003] At present, fault arc detection technology mainly relies on the extraction and analysis of electrical signal features. Common technical solutions include detection based on low-frequency current waveform distortion, detection based on high-frequency current features, and detection based on time-frequency analysis. In detection based on low-frequency current waveform distortion, the low-frequency method only focuses on the power frequency and its low-order harmonics, and it is difficult to capture the rapidly changing high-frequency characteristic signals during the arc discharge process, resulting in insufficient sensitivity and accuracy; in detection based on high-frequency current features, the limitation of single-frequency band detection is that the current harmonic characteristics of different electrical appliances vary greatly, and grid noise and electromagnetic interference from other equipment may affect specific frequency bands, leading to misjudgment; in detection based on time-frequency analysis, the time-frequency analysis method needs to analyze the signal in high-dimensional space, which has high computational complexity, and the influence of noise and grid interference may cause unstable time-frequency features. Summary of the Invention

[0004] In view of the shortcomings of existing fault arc detection methods in terms of misidentification, real-time performance, adaptability and anti-interference ability, the present invention provides a fault arc detection method and system based on frequency band analysis and parameter condition judgment to achieve efficient and accurate fault arc detection.

[0005] The present invention is achieved through the following technical solutions: A method for detecting arc faults based on frequency band analysis and parameter condition judgment is provided, the method comprising the following steps: Step S10: collecting the current signal of the circuit in the device to be detected according to the set collection cycle, and performing current data preprocessing on the collected current data of each cycle; Step S20: Convert the pre-processed current data into a frequency spectrum through Fourier transform, extract key features of the current data in the frequency domain, segment the current frequency band, perform frequency band analysis and condition judgment based on the extracted features, and determine whether the conditions for the existence of a fault arc are met; Step S30: When the arc fault condition is met, the fault cycle is accumulated and reduced through the fault cycle accumulation mechanism and the fault cycle reduction mechanism; Step S40: dynamically adjusting the fault determination period threshold value according to the RMS value of the current current, and determining whether a fault arc exists according to the dynamically adjusted fault determination period threshold value.

[0006] Preferably, the step of performing current data preprocessing on the collected current data of each cycle in step S10 includes noise suppression and filtering processing to ensure that the current data quality is good during subsequent feature extraction. After completing the noise suppression and filtering processing, the effective value RMS of the current data of the current cycle is calculated. This value reflects the energy level of the current in the current cycle and is used to determine whether the current of the cycle is within a normal range. The load state of the current cycle is determined based on the calculated RMS value. When the calculated RMS value is lower than 1.5A, it is determined that the current cycle is in a low load or no-load state and lacks sufficient signal characteristics. It is determined whether to skip further detection of the current cycle and directly enter the next cycle to avoid misjudgment or invalid analysis due to insufficient signal amplitude.

[0007] Preferably, in step S20, the pre-processed current data is converted into a frequency spectrum by Fourier transform to extract key features of the current data in the frequency domain, including: Fundamental frequency amplitude characteristics: usually the amplitude corresponding to the power frequency, which is usually 50Hz, serves as a reference for normal operation and is used for comparison and analysis of amplitude changes during the detection process; High-frequency component characteristics: Fault arcs usually produce significant changes in the high-frequency band. The cumulative amplitude change is calculated by summing the amplitudes of all frequency points in each frequency band as the high-frequency component characteristics, which is an important basis for fault arc detection. The frequency calculation range is from the 40th point, corresponding to 2000Hz, to the 512th point, corresponding to 25600Hz, with a step size of 50 points (2500Hz). Low-frequency harmonic characteristics: Remove the fundamental component and calculate the sum of the amplitudes of the remaining low-frequency harmonics (within the 10th order) to obtain the low-frequency harmonic characteristics to evaluate the degree of harmonic distortion; Total amplitude feature: Calculate the sum of the amplitudes of the entire spectrum to obtain the total amplitude feature to reflect the overall energy distribution of the current data.

[0008] Preferably, in step S20, the current frequency band is segmented, and frequency band analysis and condition judgment are performed based on the extracted features. The step of judging whether the condition for the existence of a fault arc is met includes: Current frequency band segmentation processing: From the 40th FFT point to the 512th FFT point, corresponding to 2000Hz to 25600Hz, it is divided into a certain number of frequency bands according to a fixed step size. That is, every 50 FFT points is equivalent to every 2500Hz. The amplitude sum is accumulated in each frequency band to obtain a multi-band amplitude accumulation array for the current cycle; Frequency band analysis: The cumulative amplitude results of each preset frequency band in the current cycle are compared with the cumulative amplitude values ​​of the same frequency band recorded in the normal cycle, and the rate of change of each frequency band is calculated. The rate of change reflects the abnormal increase or decrease of the amplitude of a specific frequency band in the current cycle. When the amplitude of a frequency band is higher than the normal state by a preset threshold, the frequency band is judged to be abnormal, and the number of frequency bands exceeding the threshold is counted. If the number of frequency bands that have changed is greater than 2, the frequency band abnormality condition is met in this cycle; Condition judgment: Set judgment conditions, including fundamental frequency amplitude change, total amplitude change, current effective value change and harmonic change. Fundamental frequency amplitude change means that the fundamental frequency amplitude change rate between the current cycle and the normal cycle is required to be less than 1.1 to exclude fundamental frequency fluctuations caused by normal electrical operation. First monitor the degree of harmonic distortion. Total amplitude change means that the change of total amplitude must be maintained within a certain range. Current effective value change means that the change of RMS value of the current cycle and RMS value under normal state should be controlled within a certain range. Harmonic change means calculating the difference between the total amplitude of harmonics within 10 times and the previous cycle. When the harmonic distortion rate exceeds 6%, it enters abnormal state. Constant cycle judgment further enhances the reliability of judgment. It integrates multi-band and multi-condition judgment. Only when the cumulative change of the amplitude of multiple frequency bands (at least 3 frequency bands) exceeds the set threshold and other conditions (fundamental frequency, total amplitude, RMS, etc.) meet the fault characteristics, will it be considered that a fault arc exists in the current cycle. That is, when other set judgment conditions are met at the same time, it is judged that the abnormal conditions are met in the frequency band analysis, that is, the fault arc condition is met. The cycle may have a fault arc phenomenon and the fault flag count is increased. In the next judgment, when the fault flag count is greater than 0, the harmonic distortion rate is no longer judged, and the judgment of other conditions is directly performed.

[0009] Preferably, in step S30, when the arc fault condition is met, the steps of accumulating and reducing the fault cycle by the fault cycle accumulation mechanism and the fault cycle reduction mechanism include: Fault cycle accumulation mechanism: When the current cycle detects that it meets the arc fault condition, the alarm is not immediately triggered, but the fault cycle counter is incremented. This accumulation mechanism ensures that a fault is finally confirmed only when the detection results show abnormalities for multiple consecutive cycles (the cumulative number of cycles reaches the dynamically adjusted threshold). This helps to eliminate misjudgments caused by brief fluctuations or occasional noise in a single cycle. For example, under normal circumstances, if the RMS current is less than 1.5A, the circuit is considered to be in a no-load or low-power state. In this case, the normal cycle flag count is increased. If no fault is detected for several consecutive cycles, the fault flag count is decreased, and the fault flag count is eventually restored to the initial state. Fault cycle reduction mechanism: When no abnormality is detected within a continuous cycle, the fault cycle reduction mechanism is activated to gradually reduce the fault cycle counter. The reduction mechanism ensures that the fault flag can be reduced in time after the fault state disappears or returns to normal, avoiding long-term false alarm status. At the same time, when the fault cycle counter returns to zero, the normal cycle characteristic data is updated, including the cumulative amplitude of each frequency band, fundamental frequency, total amplitude and RMS value, to adapt to slow changes in the environment or load.

[0010] Preferably, in step S40, dynamically adjusting the fault determination period threshold according to the RMS value of the current current, and determining whether a fault arc exists according to the dynamically adjusted fault determination period threshold comprises: Dynamically adjust the threshold: The arc fault determination cycle threshold is not fixed but dynamically adjusted based on the RMS value of the current. When the RMS current value is greater than 6.0A, the current is considered large, and arcing is more dangerous. Therefore, the arc fault determination cycle threshold is lowered to achieve faster response. When the RMS current value is less than or equal to 6.0A, the current is considered small, and the arc fault determination cycle threshold is restored to the default state, that is, 8 cycles. Alarm judgment: When the value of the fault cycle counter is greater than the dynamically adjusted fault arc judgment cycle threshold, it is determined that a fault arc exists and the alarm is triggered immediately; Update characteristics: When the detection result of the current cycle does not meet the alarm condition and the fault cycle counter is zero, the characteristic data of the current cycle, including the amplitude accumulation, fundamental frequency, total amplitude and RMS value of each frequency band, are updated to the new normal cycle reference data.

[0011] In addition, to achieve the above-mentioned purpose, the present invention further proposes a fault arc detection system based on frequency band analysis and parameter condition judgment, the fault arc detection system based on frequency band analysis and parameter condition judgment comprising: Current data sampling and preprocessing module: used to collect current signals according to the set collection cycle and perform current data preprocessing on the collected current data of each cycle; Feature extraction and fault diagnosis module: used to convert the pre-processed current data into a spectrum through Fourier transform, extract the key features of the current data in the frequency domain, segment the current frequency band, and perform frequency band analysis and condition judgment based on the extracted features to determine whether the conditions for the existence of a fault arc are met; Fault cycle accumulation and reduction module: used to accumulate and reduce the fault cycle through the fault cycle accumulation mechanism and the fault cycle reduction mechanism when the fault arc existence condition is met; Dynamically adjust the threshold value and alarm judgment module: used to dynamically adjust the fault judgment period threshold value according to the RMS value of the current, and judge whether there is a fault arc based on the dynamically adjusted fault judgment period threshold value.

[0012] In addition, to achieve the above-mentioned purpose, the present invention also proposes a fault arc detection device based on frequency band analysis and parameter condition judgment, and the device includes: a memory, a processor, and programs such as a fault arc detection algorithm based on frequency band analysis and parameter condition judgment stored in the memory and runnable on the processor. The programs such as the fault arc detection algorithm based on frequency band analysis and parameter condition judgment are steps for implementing the fault arc detection method based on frequency band analysis and parameter condition judgment as described above.

[0013] In addition, to achieve the above-mentioned purpose, the present invention also provides a computer program product, which includes programs such as a fault arc detection algorithm based on frequency band analysis and parameter condition judgment. When the programs such as the fault arc detection algorithm based on frequency band analysis and parameter condition judgment are executed by a processor, the fault arc detection method based on frequency band analysis and parameter condition judgment as described above is implemented.

[0014] The advantages and effects of the present invention are: This invention offers significant advantages over existing technologies in terms of adaptability, false positive rate, detection accuracy, and real-time performance. Through multi-band detection, dynamic threshold adjustment, and high sampling rate design, it can accurately and quickly detect arc faults in complex electrical environments, reducing false positives and improving fault identification reliability. It offers greater versatility and adaptability, meeting the arc detection needs of diverse electrical appliances and operating environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0016] Figure 1This is a flow chart of the arc fault detection method based on frequency band analysis and parameter condition judgment of the present invention.

[0017] Figure 2 The figure is a structural diagram of the arc fault detection system based on frequency band analysis and parameter condition judgment of the present invention.

[0018] Figure 3 This is a schematic block diagram of the structure of an electronic device for arc fault detection based on frequency band analysis and parameter condition judgment of the present invention. DETAILED DESCRIPTION

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0020] like Figure 1 As shown, in one embodiment of the present invention, a fault arc detection method based on frequency band analysis and parameter condition judgment includes the following steps: Step S10: The current signal of the circuit in the device to be tested is collected according to the set collection period, and the current data of each collected period is preprocessed. For example, a high sampling rate, such as 51.2kHz, is used to sample the current signal to ensure that the collected data can cover the frequency range from 0Hz to a maximum of 25.6kHz. The number of data points in each sampling period is set to 1024. This data length (which must be a power of 2) facilitates subsequent fast Fourier transform (FFT) processing and also improves the frequency resolution.

[0021] Specifically, the step of preprocessing the current data of each cycle collected in step S10 includes noise suppression and filtering processing to ensure that the current data quality is good during subsequent feature extraction. After completing the noise suppression and filtering processing, the effective value RMS of the current data of the current cycle is calculated. This value reflects the energy level of the current in the current cycle and is used to determine whether the current of the cycle is in a normal range. The load state of the current cycle is determined based on the calculated RMS value. When the calculated RMS value is lower than 1.5A, it is determined that the current cycle is in a low load or no-load state and lacks sufficient signal characteristics. It is determined whether to skip further detection of the current cycle and directly enter the next cycle to avoid misjudgment or invalid analysis due to insufficient signal amplitude.

[0022] Step S20: Convert the preprocessed current data into a frequency spectrum through Fourier transform, extract the key features of the current data in the frequency domain, segment the current frequency band, perform frequency band analysis and condition judgment based on the extracted features, and determine whether the conditions for the existence of a fault arc are met.

[0023] Specifically, in step S20, the pre-processed current data is converted into a frequency spectrum through Fourier transform, and key features of the current data in the frequency domain are extracted, including: Fundamental frequency amplitude characteristics: usually the amplitude corresponding to the power frequency, which is usually 50Hz, serves as a reference for normal operation and is used for comparison and analysis of amplitude changes during the detection process; High-frequency component characteristics: Fault arcs usually produce significant changes in the high-frequency band. The cumulative amplitude change is calculated by summing the amplitudes of all frequency points in each frequency band as the high-frequency component characteristics, which is an important basis for fault arc detection. The frequency calculation range is from the 40th point, corresponding to 2000Hz, to the 512th point, corresponding to 25600Hz, with a step size of 50 points (2500Hz). Low-frequency harmonic characteristics: Remove the fundamental component and calculate the sum of the amplitudes of the remaining low-frequency harmonics (within the 10th order) to obtain the low-frequency harmonic characteristics to evaluate the degree of harmonic distortion; Total amplitude feature: Calculate the sum of the amplitudes of the entire spectrum to obtain the total amplitude feature to reflect the overall energy distribution of the current data.

[0024] Specifically, in step S20, the current frequency band is segmented, and frequency band analysis and condition judgment are performed based on the extracted features. The step of judging whether the conditions for the existence of a fault arc are met includes: Current frequency band segmentation processing: From the 40th FFT point to the 512th FFT point, corresponding to 2000Hz to 25600Hz, it is divided into a certain number of frequency bands according to a fixed step size. That is, every 50 FFT points is equivalent to every 2500Hz. The amplitude sum is accumulated in each frequency band to obtain a multi-band amplitude accumulation array for the current cycle; Frequency band analysis: The cumulative amplitude results of each preset frequency band in the current cycle, such as 2000Hz–4500Hz, 4500Hz–7000Hz, and 7000Hz–9500Hz, are compared with the cumulative amplitude values ​​of the same frequency bands recorded in a normal cycle. The rate of change of each frequency band is calculated. The rate of change reflects the abnormal increase or decrease in the amplitude of a specific frequency band in the current cycle. When the amplitude of a frequency band exceeds the normal state by a preset threshold, for example, more than 1.6 times, the frequency band is judged to be abnormal. The number of frequency bands exceeding the threshold is counted. If the number of frequency bands that have changed is greater than two, the frequency band abnormality condition is met in this cycle. Conditional judgment: Set judgment conditions, including fundamental frequency amplitude change, total amplitude change, current effective value change and harmonic change. Fundamental frequency amplitude change means that the fundamental frequency amplitude change rate between the current cycle and the normal cycle is required to be less than 1.1 to exclude fundamental frequency fluctuations caused by normal electrical operation. First, monitor the degree of harmonic distortion. Total amplitude change means that the change of total amplitude must be maintained within a certain range, for example, the total amplitude change rate is not less than 0.95, to ensure that the overall energy distribution is basically stable. Current effective value change means that the change between the RMS value of the current cycle and the RMS value under normal conditions should be controlled within a certain range, for example, the change rate is less than 1.6, to avoid misjudgment due to sudden load changes. Harmonic change means calculating the difference between the total amplitude of harmonics within 10 times and the previous cycle. When the harmonic distortion rate exceeds 6 %, the system enters abnormal cycle judgment to further enhance the reliability of the judgment. It integrates multi-band and multi-condition judgment. Only when the cumulative change of the amplitudes of multiple frequency bands (at least 3 frequency bands) exceeds the set threshold and other conditions (fundamental frequency, total amplitude, RMS, etc.) meet the fault characteristics, will it be considered that a fault arc exists in the current cycle. That is, when other set judgment conditions are met at the same time, such as the fundamental amplitude change is less than 10%, the current RMS value change is less than 60%, and the total amplitude change is greater than or equal to 95%, it is judged that the abnormal conditions are met in the frequency band analysis, that is, the fault arc condition is met. The cycle may have a fault arc phenomenon and the fault flag count is increased. In the next judgment, when the fault flag count is greater than 0, the harmonic distortion rate is no longer judged, and the judgment of other conditions is directly performed.

[0025] Step S30: When the arc fault existence condition is met, the fault cycle is accumulated and reduced through the fault cycle accumulation mechanism and the fault cycle reduction mechanism.

[0026] Specifically, in step S30, when the arc fault condition is met, the steps of accumulating and reducing the fault cycle through the fault cycle accumulation mechanism and the fault cycle reduction mechanism include: Fault cycle accumulation mechanism: When the current cycle detects that it meets the arc fault condition, the alarm is not immediately triggered, but the fault cycle counter is incremented. This accumulation mechanism ensures that a fault is finally confirmed only when the detection results show abnormalities for multiple consecutive cycles (the cumulative number of cycles reaches the dynamically adjusted threshold). This helps to eliminate misjudgments caused by brief fluctuations or occasional noise in a single cycle. For example, under normal circumstances, if the RMS current is less than 1.5A, the circuit is considered to be in a no-load or low-power state. In this case, the normal cycle flag count is increased. If no fault is detected for several consecutive cycles, the fault flag count is decreased, and the fault flag count is eventually restored to the initial state. Fault cycle reduction mechanism: When no abnormality is detected within consecutive cycles, the fault cycle reduction mechanism is activated, and the fault cycle counter is gradually reduced. The reduction mechanism ensures that the fault flag can be reduced in time after the fault state disappears or returns to normal, avoiding long-term false alarm status. At the same time, when the fault cycle counter returns to zero, the normal cycle characteristic data, including the cumulative amplitude of each frequency band, fundamental frequency, total amplitude and RMS value, are updated to adapt to slow changes in the environment or load. For example, if the fault condition is not continuously met within 3 cycles, the fault cycle count will gradually decrease. When the fault cycle counter reaches the set threshold, which is 8 cycles by default and can be dynamically adjusted, a fault arc will be confirmed and an alarm signal will be triggered.

[0027] Step S40: dynamically adjusting the fault determination period threshold value according to the RMS value of the current current, and determining whether a fault arc exists according to the dynamically adjusted fault determination period threshold value.

[0028] Specifically, in step S40, dynamically adjusting the fault determination period threshold according to the RMS value of the current current, and determining whether there is a fault arc according to the dynamically adjusted fault determination period threshold includes: Dynamically adjust the threshold: The arc fault determination cycle threshold is not fixed but dynamically adjusted based on the current RMS value. When the current RMS value is greater than 6.0A, the current is considered large, and the arcing phenomenon is more dangerous. Therefore, the arc fault determination cycle threshold is lowered to achieve a faster response. When the current RMS value is less than or equal to 6.0A, the current is considered small and the arc fault determination cycle threshold is restored to the default state, that is, 8 cycles. For example, when the current RMS exceeds 6.0A, the cycle threshold is reduced by 1 for every 2.0A increase in the current RMS exceeding 6.0A. The portion less than 2.0A is calculated and processed as 2.0A. However, the minimum cycle cannot be less than 4 cycles, that is, 0.08s, to avoid misjudgment. Alarm judgment: When the value of the fault cycle counter is greater than the dynamically adjusted fault arc judgment cycle threshold, it is determined that a fault arc exists and an alarm is immediately triggered, such as calling an alarm pulse signal; Update characteristics: When the detection results of the current cycle do not meet the alarm conditions and the fault cycle counter is zero, the characteristic data of the current cycle, including the amplitude accumulation, fundamental frequency, total amplitude and RMS value of each frequency band, are updated to the new normal cycle reference data. In addition, regardless of the value of the fault counter, the low-frequency harmonic amplitude of the current cycle is saved and extracted for comparison in the next cycle. This update mechanism enables the system to adapt to the slow characteristic adjustments caused by load changes, environmental changes, etc. during the long-term operation of electrical appliances, thereby improving the accuracy and adaptability of subsequent detection.

[0029] In addition, if Figure 2As shown, in one embodiment of the present invention, a fault arc detection system based on frequency band analysis and parameter condition judgment is proposed. The fault arc detection system based on frequency band analysis and parameter condition judgment includes: Current data sampling and preprocessing module: used to collect current signals according to the set collection cycle and perform current data preprocessing on the collected current data of each cycle; Feature extraction and fault diagnosis module: used to convert the pre-processed current data into a spectrum through Fourier transform, extract the key features of the current data in the frequency domain, segment the current frequency band, and perform frequency band analysis and condition judgment based on the extracted features to determine whether the conditions for the existence of a fault arc are met; Fault cycle accumulation and reduction module: used to accumulate and reduce the fault cycle through the fault cycle accumulation mechanism and the fault cycle reduction mechanism when the fault arc existence condition is met; Dynamically adjust the threshold value and alarm judgment module: used to dynamically adjust the fault judgment period threshold value according to the RMS value of the current, and judge whether there is a fault arc based on the dynamically adjusted fault judgment period threshold value.

[0030] The arc fault detection system based on frequency band analysis and parameter condition judgment provided in this application utilizes the arc fault detection method based on frequency band analysis and parameter condition judgment in the above-mentioned embodiments, and can address the technical issues of poor real-time performance, adaptability, and anti-interference capabilities of existing arc fault detection methods. Compared with the prior art, the beneficial effects of the arc fault detection system based on frequency band analysis and parameter condition judgment provided in this application are the same as those of the arc fault detection method based on frequency band analysis and parameter condition judgment provided in the above-mentioned embodiments. Other technical features of the arc fault detection system based on frequency band analysis and parameter condition judgment are the same as those disclosed in the above-mentioned embodiments and are not further described here.

[0031] The present application provides a fault arc detection device based on frequency band analysis and parameter condition judgment, which can be applied to an intelligent measuring switch. The fault arc detection device based on frequency band analysis and parameter condition judgment includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the fault arc detection method based on frequency band analysis and parameter condition judgment in the above-mentioned embodiment one.

[0032] like Figure 3In one embodiment of the present invention, a schematic diagram of the structure of a fault arc detection device based on frequency band analysis and parameter condition judgment suitable for implementing the embodiments of the present application is shown. The fault arc detection device based on frequency band analysis and parameter condition judgment in the embodiments of the present application can include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), etc., as well as fixed terminals such as digital TVs and desktop computers. Figure 3 The arc fault detection device based on frequency band analysis and parameter condition judgment shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0033] Figure 3 The illustrated arc fault detection device based on frequency band analysis and parameter condition determination may include a processor 1001 (e.g., a central processing unit, graphics processing unit, etc.), which can execute various appropriate actions and processes based on programs stored in a read-only memory (ROM) 1002 or loaded from a storage device 1003 into a machine-readable storage medium (RAM) 1004. RAM 1004 also stores various programs and data required for the operation of the arc fault detection device based on frequency band analysis and parameter condition determination. Processor 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, hard disk, etc.; and a communication unit 1009. The communication unit 1009 can allow the arc fault detection device based on frequency band analysis and parameter condition judgment to communicate wirelessly or wired with other devices to exchange data. Although the figure shows an arc fault detection device based on frequency band analysis and parameter condition judgment with various systems, it should be understood that implementation or presence of all the illustrated systems is not required. More or fewer systems may alternatively be implemented or present.

[0034] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication system, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processor 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are performed.

[0035] The arc fault detection device based on frequency band analysis and parameter condition judgment provided in this application adopts the arc fault detection method based on frequency band analysis and parameter condition judgment in the above-mentioned embodiment, which can solve the technical problems of existing arc fault detection methods such as poor real-time performance, adaptability, and anti-interference capabilities. Compared with the existing technology, the beneficial effects of the arc fault detection device based on frequency band analysis and parameter condition judgment provided in this application are the same as the beneficial effects of the arc fault detection method based on frequency band analysis and parameter condition judgment provided in the above-mentioned embodiment. The other technical features of the arc fault detection device based on frequency band analysis and parameter condition judgment are the same as those disclosed in the method of the above-mentioned embodiment, and are not further described here.

[0036] The various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any appropriate manner in any one or more embodiments or examples.

[0037] The present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for arc fault detection based on frequency band analysis and parameter condition judgment.

[0038] The computer program product provided in this application can address the technical issues of existing arc fault detection methods, such as poor real-time performance, adaptability, and anti-interference capabilities. Compared to existing technologies, the computer program product provided in this application has the same beneficial effects as the arc fault detection method based on frequency band analysis and parameter condition judgment provided in the above-mentioned embodiments, and will not be further elaborated here.

[0039] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A fault arc detection method based on frequency band analysis and parameter condition judgment, characterized in that: The method comprises: Step S10: collecting the current signal of the circuit in the device to be detected according to the set collection cycle, and performing current data preprocessing on the collected current data of each cycle; Step S20: Convert the pre-processed current data into a frequency spectrum through Fourier transform, extract the characteristics of the current data in the frequency domain, segment the current frequency band, perform frequency band analysis and condition judgment based on the extracted characteristics, and determine whether the conditions for the existence of a fault arc are met; Step S30: When the arc fault condition is met, the fault cycle is accumulated and reduced through the fault cycle accumulation mechanism and the fault cycle reduction mechanism; Step S40: dynamically adjusting the fault determination period threshold value according to the RMS value of the current current, and determining whether a fault arc exists according to the dynamically adjusted fault determination period threshold value.

2. The arc fault detection method based on frequency band analysis and parameter condition judgment according to claim 1 is characterized in that: The step of preprocessing the collected current data of each cycle in step S10 includes noise suppression and filtering. After the noise suppression and filtering are completed, the effective value RMS of the current data of the current cycle is calculated, and the load state of the current cycle is judged based on the calculated RMS value to determine whether to skip further detection of the current cycle and directly enter the next cycle.

3. The arc fault detection method based on frequency band analysis and parameter condition judgment according to claim 1, characterized in that: In step S20, the pre-processed current data is converted into a frequency spectrum by Fourier transform, and the features of the current data in the frequency domain are extracted, including: Fundamental frequency amplitude characteristic: It is the amplitude corresponding to the power frequency and serves as a reference for normal operation; High-frequency component characteristics: The fault arc changes in the high-frequency band. The cumulative amplitude change is obtained by calculating the sum of the amplitudes of all frequency points in each frequency band as the high-frequency component characteristics; Low-frequency harmonic characteristics: Remove the fundamental component and calculate the sum of the remaining low-frequency harmonic amplitudes to obtain the low-frequency harmonic characteristics; Total amplitude feature: Calculate the sum of the amplitudes of the entire spectrum to obtain the total amplitude feature.

4. The arc fault detection method based on frequency band analysis and parameter condition judgment according to claim 1, characterized in that: In step S20, the current frequency band is segmented, and frequency band analysis and condition judgment are performed based on the extracted features. The step of judging whether the condition for the existence of a fault arc is met includes: Current frequency band segmentation processing: From the 40th FFT point to the 512th FFT point, corresponding to 2000Hz to 25600Hz, it is divided into a certain number of frequency bands according to a fixed step size. That is, every 50 FFT points is equivalent to every 2500Hz. The amplitude sum is accumulated in each frequency band to obtain a multi-band amplitude accumulation array for the current cycle; Frequency band analysis: The cumulative amplitude results of each preset frequency band in the current cycle are compared with the cumulative amplitude values ​​of the same frequency band recorded in the normal cycle, and the rate of change of each frequency band is calculated. The rate of change reflects the abnormal increase or decrease of the amplitude of a specific frequency band in the current cycle. When the amplitude of a frequency band is higher than the normal state by a preset threshold, the frequency band is judged to be abnormal, and the number of frequency bands exceeding the threshold is counted. If the number of frequency bands that have changed is greater than 2, the frequency band abnormality condition is met in this cycle; Conditional Judgment: Set judgment conditions, including fundamental frequency amplitude change, total amplitude change, current RMS change, and harmonic change. Fundamental frequency amplitude change requires that the rate of change of the fundamental frequency amplitude between the current cycle and the normal cycle be less than 1.

1. Total amplitude change means that the change in the total amplitude remains within a certain range. Current RMS change means that the change between the RMS value of the current cycle and the RMS value under normal conditions is controlled within a certain range. Harmonic change refers to the difference between the total amplitude of harmonics within 10 orders and the previous cycle. When the harmonic distortion rate exceeds 6%, the abnormal cycle judgment is initiated. When other set judgment conditions are met at the same time, the frequency band analysis is judged to meet the abnormal condition, that is, the fault arc condition is met, and the fault flag count is increased. In the next judgment, when the fault flag count is greater than 0, the harmonic distortion rate is no longer judged, and the other conditions are judged directly.

5. The arc fault detection method based on frequency band analysis and parameter condition judgment according to claim 1, characterized in that: In step S30, when the arc fault condition is met, the steps of accumulating and reducing the fault cycle by the fault cycle accumulation mechanism and the fault cycle reduction mechanism include: Accumulation mechanism of fault cycles: when it is detected that the current cycle meets the fault arc condition, the alarm is not triggered immediately, but the fault cycle counter is incremented; Fault cycle reduction mechanism: When no abnormality is detected in consecutive cycles, the fault cycle reduction mechanism is activated to reduce the fault cycle counter. At the same time, when the fault cycle counter returns to zero, the normal cycle characteristic data is updated.

6. The arc fault detection method based on frequency band analysis and parameter condition judgment according to claim 1, characterized in that: In step S40, the step of dynamically adjusting the fault determination period threshold according to the RMS value of the current current and determining whether there is a fault arc according to the dynamically adjusted fault determination period threshold comprises: Dynamically adjust the threshold: The arc fault determination cycle threshold is dynamically adjusted according to the RMS value of the current. When the current RMS value is greater than 6.0A, the arc fault determination cycle threshold is lowered. When the current RMS value is less than or equal to 6.0A, the arc fault determination cycle threshold is restored to the default state. Alarm judgment: When the value of the fault cycle counter is greater than the dynamically adjusted fault arc judgment cycle threshold, it is determined that a fault arc exists and the alarm is triggered immediately; Update characteristics: When the detection result of the current cycle does not meet the alarm condition and the fault cycle counter is zero, the characteristic data of the current cycle, including the amplitude accumulation, fundamental frequency, total amplitude and RMS value of each frequency band, are updated to the new normal cycle reference data.

7. The arc fault detection system based on frequency band analysis and parameter condition judgment is characterized by: The method for detecting arc faults based on frequency band analysis and parameter condition judgment according to claim 1 comprises: Current data sampling and preprocessing module: used to collect current signals according to the set collection cycle and perform current data preprocessing on the collected current data of each cycle; Feature extraction and fault judgment module: used to convert the pre-processed current data into a spectrum through Fourier transform, extract the characteristics of the current data in the frequency domain, segment the current frequency band, perform frequency band analysis and condition judgment based on the extracted characteristics, and determine whether the conditions for the existence of a fault arc are met; Fault cycle accumulation and reduction module: used to accumulate and reduce the fault cycle through the fault cycle accumulation mechanism and the fault cycle reduction mechanism when the fault arc existence condition is met; Dynamically adjust the threshold value and alarm judgment module: used to dynamically adjust the fault judgment period threshold value according to the RMS value of the current, and judge whether there is a fault arc based on the dynamically adjusted fault judgment period threshold value.

8. Fault arc detection equipment based on frequency band analysis and parameter condition judgment, characterized in that: include: A memory, a processor, and a fault arc detection program based on frequency band analysis and parameter condition judgment stored in the memory and executable on the processor, wherein the fault arc detection program based on frequency band analysis and parameter condition judgment, when executed by the processor, implements the fault arc detection method based on frequency band analysis and parameter condition judgment as described in any one of claims 1 to 6.

9. A computer program product, characterized in that It comprises a fault arc detection program based on frequency band analysis and parameter condition judgment, which, when executed by a processor, implements the fault arc detection method based on frequency band analysis and parameter condition judgment as described in any one of claims 1 to 6.

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